Characterizing In-Situ Metatarsal Fracture Risk During Simulated Workplace Impact Loading
Bibliographic record
Abstract
Metatarsal fractures represent the most common traumatic foot injury; however, metatarsal fracture thresholds remain poorly characterized, which affects performance targets for protective footwear. This experimental study investigated impact energies, forces, and deformations to characterize metatarsal fracture risk for simulated in situ workplace impact loading. A drop tower setup conforming to ASTM specifications for testing impact resistance of metatarsal protective footwear applied a target impact load (22-55 J) to 10 cadaveric feet. Prior to impact, each foot was axially loaded through the tibia with a specimen-specific bodyweight load to replicate a natural weight-bearing stance. Successive iterations of impact tests were performed until a fracture was observed with X-ray imaging. Descriptive statistics were computed for force, deformation, and impact energy. Correlational analysis was conducted on donor age, BMI, deformation, force, and impact energy. A survival analysis was used to generate injury risk curves (IRC) using impact energy and force. All 10 specimens fractured with the second metatarsal being the most common fracture location. The mean peak energy, force, and deformation during fracture were 46.6 J, 4640 N, 28.9 mm, respectively. Survival analyses revealed a 50% fracture probability was associated with 35.8 J and 3562 N of impact. Foot deformation was not significantly correlated (p = 0.47) with impact force, thus deformation is not recommended to predict metatarsal fracture risk. The results from this study can be used to improve test standards for metatarsal protection, provide performance targets for protective footwear developers, and demonstrate a methodological framework for future metatarsal fracture research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".